Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

Automatic Detection of Rice Blast Fungus Spores by Deep Learning-Based Object Detection: Models, Benchmarks and Quantitative Analysis

The severity of rice blast and its impacts on rice yield are closely related to the inoculum quantity of <i>Magnaporthe oryzae</i>, and automatic detection of the pathogen spores in microscopic images can provide a rapid and effective way to quantify pathogen inoculum. Traditional spore...

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Bibliographic Details
Main Authors: Huiru Zhou, Qiang Lai, Qiong Huang, Dingzhou Cai, Dong Huang, Boming Wu
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Agriculture
Subjects:
rice blast
fungal spore detection
object detection
deep learning
disease monitoring
Online Access:https://www.mdpi.com/2077-0472/14/2/290

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https://www.mdpi.com/2077-0472/14/2/290

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